Top Remote Data & Analytics Jobs in Nashville, TN

Design and build PerfectServe’s unified cloud data platform, including ingestion pipelines, integrations, data models, metrics, and consumer-facing data layers. Establish architecture, engineering, quality, and testing standards; enable safe extraction from production systems; partner with data users; and mentor data engineers. The role requires substantial cloud data platform experience, deep SQL, warehouse expertise, ELT, change-data-capture, AWS, modeling, orchestration, and data quality skills.

This role involves writing user stories, supporting agile team processes, and driving customer discovery to enhance technology solutions for clients.

Leads the design, development, and maintenance of scalable AWS data platforms, pipelines, ETL/ELT workflows, data models, and orchestration systems. Uses Python, PySpark, SQL, dbt, AWS services, modern lakehouse technologies, and CI/CD practices to deliver secure federal data solutions. Responsibilities include platform modernization, performance optimization, AI-enabled data integration, compliance support, technical mentoring, architecture discussions, and cross-functional Agile collaboration.

The Business Analyst gathers and prioritizes business requirements, supports technology platform design and implementation, maintains process and stakeholder knowledge, conducts software testing and quality assurance, assists with project planning, and trains end users. The role collaborates with vendors, IT, operations, underwriting, and distribution teams to align technology solutions with business needs, KPIs, and the organization’s technology roadmap.

Staff Data Engineer responsible for defining data architecture strategy, building fault-tolerant batch and real-time streaming infrastructure, and managing scalable lakehouse and database systems. The role processes telemetry from millions of connected devices, drives performance tuning and data quality, supports on-call operations, evaluates modern technologies, and mentors engineers. It requires strong expertise in Python, SQL, cloud data platforms, streaming systems, infrastructure as code, and enterprise-scale architecture.

Designs and manages Qualtrics in-product intercept surveys, targeting workflows, directories, and data collection. Analyzes user behavior, product performance, and survey feedback; builds dashboards and communicates actionable insights to product, UX, and leadership teams. Collaborates with qualitative researchers, data engineering, and development teams to integrate survey data with behavioral analytics platforms and enable access to behavioral datasets across B2B and B2C products.

Designs and implements Hightouch Identity Resolution solutions for enterprise customers. Assesses data architectures, develops identity rules and data models, debugs customer issues using SQL and server logs, explains system behavior, and identifies product gaps. Partners with sales engineering, support, and technical account management while advising technical and executive stakeholders on data strategy and identity architecture.

Build and maintain finance data pipelines, warehouse models, dashboards, and automations for unit economics, technology budgeting, burn, runway, margins, deal desk, and sales compensation. Develop internal tools and LLM-based agents that help non-engineers access trusted financial data. Partner with finance, sales, and engineering leadership to reconcile discrepancies and improve revenue, cost, billing, payroll, cloud-spend, and accounting workflows.

Build and manage scalable ETL/ELT pipelines using Microsoft Fabric and Azure Databricks, implement medallion architecture, support data migrations and legacy SSRS decommissioning, develop Power BI data models, and maintain governance, security, and CI/CD practices. The role also supports migration events and modernizes data infrastructure for advanced analytics and reporting.

Analyze contributor performance, dataset quality, production efficiency, and client delivery metrics. Build dashboards and reports, conduct cohort and trend analysis, monitor quality benchmarks, identify data integrity issues, and translate findings into actionable recommendations. Collaborate with Data Operations, Engineering, Product, and Quality Assurance teams to define metrics and evaluate improvements.